## Logistic线性回归
from numpy import *

def loadDataSet():
    dataMat = []
    labelMat = []
    fr = open('testSet.txt')
    for line in fr.readlines():
        lineArr = line.strip().split()
        dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
        labelMat.append(int(lineArr[2]))
    return dataMat, labelMat

## sigmoid函数
def sigmoid(inX):
    return 1.0 / (1 + exp(-inX))

def gradAscent(dataMatIn, classLabels):
    dataMatrix = mat(dataMatIn)
    labelMat = mat(classLabels).transpose()
    m, n = shape(dataMatrix)
    alpha = 0.001
    maxCycles = 500
    weights = ones((n, 1))
    # 进行maxCycles次循环然后逐步趋向理想点，weights得到的是回归系数(可读性可能不太好)
    for k in range(maxCycles):
        h = sigmoid(dataMatrix * weights)
        error = labelMat - h
        weights = weights + alpha * dataMatrix.transpose() * error
    return weights


# 画出决策边界
def plotBestFit(weights):
    import matplotlib.pyplot as plt
    dataMat, labelMat = loadDataSet()
    dataArr = array(dataMat)
    n = shape(dataArr)[0]
    xcord1 = []
    ycord1 = []
    xcord2 = []
    ycord2 = []
    for i in range(n):
        if int(labelMat[i]) == 1:
            xcord1.append(dataArr[i, 1])
            ycord1.append(dataArr[i, 2])
        else:
            xcord2.append(dataArr[i, 1])
            ycord2.append(dataArr[i, 2])
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.scatter(xcord1, ycord1, s=30, c='red', marker='s')
    ax.scatter(xcord2, ycord2, s=30, c='green')
    x = arange(-3.0, 3.0, 0.1)
    y = (-weights[0] - weights[1] * x) / weights[2]
    ax.plot(x, y)
    plt.xlabel('X1')
    plt.ylabel('X2')
    plt.show()


# 随机梯度上升算法
def stocGradAscent0(dataMatrix, classLabels):
    m, n = shape(dataMatrix)
    alpha = 0.01
    weights = ones(n)
    for i in range(m):
        h = sigmoid(sum(dataMatrix[i] * weights))
        error = classLabels[i] - h
        weights = weights + alpha * error * dataMatrix[i]
    return weights


# 改进的随机梯度上升算法，默认迭代150次
def stocGradAscent1(dataMatrix, classLabels, numIter=150):
    m, n = shape(dataMatrix)
    weights = ones(n)
    for j in range(numIter):
        dataIndex = list(range(m))
        for i in range(m):
            alpha = 4 / (1.0 + j + i) + 0.01
            randIndex = int(random.uniform(0, len(dataIndex)))
            h = sigmoid(sum(dataMatrix[randIndex] * weights))
            error = classLabels[randIndex] - h
            weights = weights + alpha * error * dataMatrix[randIndex]
            del(dataIndex[randIndex])
    return weights


## 从疝气病症判断马的致死率
# 首先创造通过sigmoid函数大小确定标签
def classfiyVector(inX, weights):
    prob = sigmoid(sum(inX * weights))
    if prob > 0.5:
        return 1.0
    else:
        return 0.0

# 返回一个错误率
def colicTest():
    frTrain = open('./horseColicTraining.txt')
    frTest = open('./horseColicTest.txt')
    trainingSet = []
    trainingLabels = []
    for line in frTrain.readlines():
        currLine = line.strip().split('\t')
        lineArr = []
        for i in range(21):
            lineArr.append(float(currLine[i]))
        # 将训练集放入trainingSet中
        trainingSet.append(lineArr)
        # 将每一行的标签放入trainingLabels中，即第22个元素
        trainingLabels.append(float(currLine[21]))
    # 得到回归系数trainWeights，使用500次迭代
    trainWeights = stocGradAscent1(array(trainingSet), trainingLabels, 500)
    errorCount = 0
    numTestVec = 0.0
    for line in frTest.readlines():
        numTestVec += 1.0
        currLine = line.strip().split('\t')
        lineArr = []
        for i in range(21):
            lineArr.append(float(currLine[i]))
        if int(classfiyVector(array(lineArr), trainWeights)) != int(currLine[21]):
            errorCount += 1
    errorRate = (float(errorCount) / numTestVec)
    print('the error rate of this test is: %f' % errorRate)
    return errorRate

# 计算平均的错误率，每次算一次错误率结果，总共算10次
def multiTest():
    numTests = 10
    errorSum = 0.0
    for k in range(numTests):
        errorSum += colicTest()
    print('after %d iterations the average error rate is: %f' % (numTests, errorSum / float(numTests)))
